A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring
The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effe...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/5/1/19 |
| _version_ | 1797242326519644160 |
|---|---|
| author | Elham Albaroudi Taha Mansouri Ali Alameer |
| author_facet | Elham Albaroudi Taha Mansouri Ali Alameer |
| author_sort | Elham Albaroudi |
| collection | DOAJ |
| description | The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society. |
| first_indexed | 2024-04-24T18:37:27Z |
| format | Article |
| id | doaj.art-2c96074b37b548cf82adb09a766c7a83 |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-04-24T18:37:27Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj.art-2c96074b37b548cf82adb09a766c7a832024-03-27T13:17:12ZengMDPI AGAI2673-26882024-02-015138340410.3390/ai5010019A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job HiringElham Albaroudi0Taha Mansouri1Ali Alameer2School of Science, Engineering and Environment, University of Salford, University Road, Manchester M5 4QJ, UKSchool of Science, Engineering and Environment, University of Salford, University Road, Manchester M5 4QJ, UKSchool of Science, Engineering and Environment, University of Salford, University Road, Manchester M5 4QJ, UKThe study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.https://www.mdpi.com/2673-2688/5/1/19algorithmic biasdeep learningcurriculum vitae screeningnatural language processingartificial intelligence |
| spellingShingle | Elham Albaroudi Taha Mansouri Ali Alameer A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring AI algorithmic bias deep learning curriculum vitae screening natural language processing artificial intelligence |
| title | A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring |
| title_full | A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring |
| title_fullStr | A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring |
| title_full_unstemmed | A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring |
| title_short | A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring |
| title_sort | comprehensive review of ai techniques for addressing algorithmic bias in job hiring |
| topic | algorithmic bias deep learning curriculum vitae screening natural language processing artificial intelligence |
| url | https://www.mdpi.com/2673-2688/5/1/19 |
| work_keys_str_mv | AT elhamalbaroudi acomprehensivereviewofaitechniquesforaddressingalgorithmicbiasinjobhiring AT tahamansouri acomprehensivereviewofaitechniquesforaddressingalgorithmicbiasinjobhiring AT alialameer acomprehensivereviewofaitechniquesforaddressingalgorithmicbiasinjobhiring AT elhamalbaroudi comprehensivereviewofaitechniquesforaddressingalgorithmicbiasinjobhiring AT tahamansouri comprehensivereviewofaitechniquesforaddressingalgorithmicbiasinjobhiring AT alialameer comprehensivereviewofaitechniquesforaddressingalgorithmicbiasinjobhiring |